A Study Using Sequence Comparison to Investigate theMolecular Evolution of Mitochondrial tRNA Genes
Jun-Ichi Sagara

Shugo Nakamura

jun@bi.a.u-tokyo.ac.jp

shugo@bi.a.u-tokyo.ac.jp

Mitsunori Ikeguchi

Kentaro Shimizu

ike@bi.a.u-tokyo.ac.jp

shimizu@bi.a.u-tokyo.ac.jp

Department of Biotechnology, The University of Tokyo
1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657 Japan

1 Introduction
In investigating the origins of life and molecular evolution, it is very important to study structureand functions that are common to distantly related species and to analyze the corresponding genesequences, which are called \molecular fossils." We therefore developed a computational method [1]based on principal component analysis (PCA) and multidimensional scaling analysis (MDS), and in
the present study have used it to detect bases characterizing speci c sequences mitochondrial transfer
RNA. Our methods rst classify the sequence in a genomic database into groups by PCA of multiple
sequence alignment: Gene sequences are represented as vectors in a generalized sequence space, andgroups of similar sequences are revealed when these vectors are projected onto a lower-dimensionalsequence space. The distribution of bases is then compared with the distribution of sequences by using
MDS, in which the bases of each sequence are projected individually onto the same sequence space.
This makes it possible to identify bases characteristic of each group. Applying this method to thesequences of all the mitochondrial tRNA genes, we expect to detect not only bases that are always
conserved but also bases that are often conserved.

2 MethodOur method is based on the one used by the Casari et al. [2] to predict functional residues in protein
families. We have extended it to the analysis of tRNA gene sequences and have used it to identify thegroups of bases speci c to particular species by applying its basic procedure recursively.
The procedure of our method is as follows:
1. Apply the PCA to the entire sequences of tRNA genes. Each sequence is plotted on a twodimensional plane called the sequence space.
2. Apply the MDS. Bases of each sequence are projected individually onto the same two-dimensional
plane in order to trace the principal components back to individual bases and positions thatcharacterize individual groups.
3. Apply the above two classi cation and comparison steps recursively. This enables us to classify
the groups into subgroups and makes the results of the classi cation clearer.
In evaluating our method, we used full-length mitochondrial tRNA gene sequences deposited in
the aligned sequence database European Bioinformatics Institute (EBI) Data library [3].

3 Results
We detected many bases characteristic of all the mitochondrial tRNA gene sequences of various species.
Most of the characteristic bases are in stem regions, and most of the characteristic bases that are not353

Figure 1: Histogram of characteristic bases in mitochondrial tRNA genes.
Table 1: Number of bases in each region.
numberfraction

stems D-loop anticodon-loop extra-loop T-loop other regions
873
106
39
2
82
48
0.759 0.092
0.034
0.002
0.071
0.042

in stem regions are in T and D domains, which are elbow regions of tRNAs (Fig. 1 and Table 1). The
results suggest that the characteristic bases in these stems and domains have a role of preserving theL-shape structure of each tRNA.

References[1] Sagara, J.-I., Shimizu, S., Kawabata, T., Nakamura, S., Ikeguchi, M., and Shimizu, K., The useof sequence comparison to detect `identities' in tRNA genes, Nucleic Acids Res., 26:1974{1979,1998.
[2] Casari, G., Sander, C. and Valencia, A., A method to predict functional residues in proteins,
Natl. Struc. Biol., 2:171{178, 1995.
[3] Sprintzl, M., Steegborn, C., Hubel, F., and Steinberg, S., Compilation of tRNA sequences andsequences of tRNA genes, Nucleic Acids Res., 24:68{72, 1996.

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